HIGH-SPEED LOCATION METHOD FOR HIGH IMPEDANCE FAULT IN DISTRIBUTION NETWORK

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001]This application claims priority to Chinese Patent Application No. 202411837576.3, titled “HIGH-SPEED LOCATION METHOD FOR HIGH IMPEDANCE FAULT IN DISTRIBUTION NETWORK”, filed on Dec. 13, 2024, the entire disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

[0002]The present application relates to the technical field of power system fault detection, and in particular to a high-speed location method for high impedance fault in distribution network based on directional frequency scanning.

BACKGROUND

[0003]High impedance fault in distribution network is one of the most common and difficult fault types to detect. This kind of fault is usually manifested as a very weak current signal, which is often only in the order of several amperes, and is easily concealed by various noise signals in the distribution network. Therefore, it is a great challenge in the operation and maintenance of distribution network to accurately extract the characteristics of high impedance fault and quickly locate the fault location.

[0004]Traditional methods usually rely on the measurement data of current and voltage transformers, and identify and locate the fault point through centralized calculation. However, these methods are not stable enough to extract fault features because of their sensitivity to noise. In addition, a large number of measurement data need to be transmitted to the main station for centralized processing, which not only increases the burden of data transmission, but also brings a large delay, which is difficult to meet the requirements of rapid response to faults, and installing expensive voltage transformers at all measuring points will lead to a large amount of hardware investment and expensive maintenance costs.

SUMMARY

[0005]This application addresses the limitations of current technology by presenting a space vector projection method, aiming at solving the problem that the traditional method usually relies on the measurement data of current and voltage transformers at the same time, and installing expensive voltage transformers at all measuring points will lead to a large amount of hardware investment and expensive maintenance costs.

[0006]In the first aspect, the present application adopts the following technical solutions.

$❘U\left(z\right)-U\left(z-4\right)❘<9\&\&❘U\left(z-15\right)-U\left(z-30\right)❘<4$

wherein U(z) is a discrete sampling value of the bus zero-sequence voltage, z is a index number of the corresponding sampling time, U(z−4) is the discrete sampling value of bus zero-sequence voltage with the corresponding index number z−4, U(z−15) is the discrete sampling value of bus zero-sequence voltage with corresponding index number z−15, and U(z−30) is the discrete sampling value of bus zero-sequence voltage with corresponding index number z−30.

[0016]In an embodiment, the decomposing discrete Fourier includes: performing Fourier expansion on discrete time series I_0j[n] with length N:

${\mathrm{IMF}}_1=I_{0j}^{\mathrm{Re}}.$

$\text{?}\begin{array}{c}\stackrel{\text{'}}{a}{I}_{0j}^{\mathrm{Re},0.5T},a^v\tilde{n}=\arccos \frac{I_{0j}^{\mathrm{Re},0.5T}\text{?}}{I_{0j}^{\mathrm{Re},0.5T}\text{?}a^v}\\ \stackrel{\text{'}}{a}{I}_{0j}^{\mathrm{Re},0.5T},b^v\tilde{n}=\arccos \frac{I_{0j}^{\mathrm{Re},0.5T}\text{?}}{I_{0j}^{\mathrm{Re},0.5T}\text{?}b^v}\end{array}.$$\text{?}\text{indicates text missing or illegible when filed}$

[0028]In an embodiment, in the S4 step, a result of a feature vector generation is expressed in polar coordinates: S403, obtaining a set of three-dimensional vectors as follows:

$\text{?}\begin{array}{c}{P}_{j,a^v}^v=\left(❘I_{0j}^{\mathrm{Re},0.5T}❘,\stackrel{\text{'}}{a}{I}_{0j}^{\mathrm{Re},0.5T},a^v\tilde{n}\right)\\ P_{j,b^v}^v=\left(❘I_{0j}^{\mathrm{Re},0.5T}❘,\stackrel{\text{'}}{a}{I}_{0j}^{\mathrm{Re},0.5T},b^v\tilde{n}+p\right)\end{array}$$\text{?}\text{indicates text missing or illegible when filed}$

[0030]S404, taking all j=1, 2, 3, . . . , J, if the following inequality can be established:

$S\left(i\right)=\frac{b\left(i\right)-a\left(i\right)}{\max \left\{a\left(i\right),b\left(i\right)\right\}};$

wherein a(i) represents a cohesion of sample points, which is calculated as follows:

$a\left(i\right)=\frac{1}{n-1}\sum _{j\ne i}^n\mathrm{distance}\left(i,j\right)$

wherein j represents other sample point in the same class as sample i, and distance represents distance between i and j, and the smaller a(i) means that the class is more compact, the calculation method of b(i) is similar to that of a(i), but it is necessary to traverse other clusters to get multiple values {b₁(i), b₂(i), b₃(i), . . . , b_m(i)}, and choose the smallest value as the final result;

$S\left(i\right)\text{:}S\left(i\right)=\{\begin{array}{cc}1-\frac{a\left(i\right)}{b\left(i\right)}& a\left(i\right)<b\left(i\right)\\ 0& a\left(i\right)=b\left(i\right)\\ \frac{b\left(i\right)}{a\left(i\right)}-1& a\left(i\right)>b\left(i\right)\end{array}$

[0040]In the second aspect, the present application adopts the following technical solutions.

[0041]A system for quickly locating the high impedance fault in distribution network based on directional frequency scanning, wherein the system is used for a high-speed location method of the high impedance fault in the distribution network based on the directional frequency scanning, including: signal acquisition module, and the signal acquisition module is used for acquiring a zero-sequence current signal I_0j collected by each of monitoring points of the distribution network and a bus zero-sequence voltage signal U₀ of the distribution network to provide basic data for fault location; fault time determination module, and the fault time determination module is used for analyzing the bus zero-sequence voltage signal U₀ and determining an accurate fault occurrence time t_f by setting a threshold value and a reverse search algorithm; feature extraction module, and the feature extraction module is used for performing discrete Fourier transform on the zero-sequence current signal I_0j and extracting a first natural mode function for reconstructing a fault signal feature; feature vector construction module, and the feature vector construction module is used for generating a reference vector in a high-dimensional space, projecting a first natural mode function to an orthogonal vector group, constructing a three-dimensional feature vector, and intuitively representing a fault feature; and feature fusion and analysis module, and the feature fusion and analysis module is used for analyzing a direction vector based on a fuzzy clustering algorithm, calculating a contour coefficient, and locating a fault section in combination with a network topology.

[0042]In the third aspect, the present application adopts the following technical solutions.

[0043]A computer device includes a memory; a processor; and a computer program stored in the memory and operable on the processor, wherein the processor executes the computer program to realize a high-speed location method of high impedance fault in distribution network based on directional frequency scanning.

[0044]In the fourth aspect, the present application adopts the following technical solutions.

[0045]A readable storage medium, wherein a computer program is stored on the readable storage medium, and the computer program is executed by a processor to realize a high-speed location method of high impedance fault in distribution network based on directional frequency scanning.

Beneficial Effects of the Present Application

BRIEF DESCRIPTION OF THE DRAWINGS

[0050]FIG. 1 is a flowchart of high-speed location method of high impedance fault in distribution network based on directional frequency scanning of the present application.

[0051]FIG. 2 is a schematic diagram of seven zero-sequence current sampling points distributed in a 10 kV medium-voltage distribution system of the present application.

[0052]FIG. 3 is a schematic diagram of an ideal zero-sequence current sampling signal without considering the influence of noise interference at each measuring point of the present application.

[0053]FIG. 4 is a schematic diagram of characteristic vector groups in polar coordinates under different fault angles of the present application.

[0054]FIG. 5 is a schematic diagram of the polar coordinate form feature vector group under the interference of −2.8 dB strong noise of the present application.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055]In order to enable those in the technical field better understand the solutions of the present application, the technical solutions in the embodiment of the present application will be described clearly and completely with the attached drawings. Obviously, the described embodiment is only a part of the embodiment of the present application, but not the whole embodiment. Based on the embodiment in this application, all other embodiment obtained by ordinary technicians in this field without creative work should belong to the protection scope of this application.

Embodiment 1

$❘U\left(z\right)-U\left(z-4\right)❘<9\&\&❘U\left(z-15\right)-U\left(z-30\right)❘<4$

wherein U(z) is a discrete sampling value of the bus zero-sequence voltage, z is a index number of the corresponding sampling time, U(z−4) is the discrete sampling value of bus zero-sequence voltage with the corresponding index number z−4, U(z−15) is the discrete sampling value of bus zero-sequence voltage with corresponding index number z−15, and U(z−30) is the discrete sampling value of bus zero-sequence voltage with corresponding index number z−30.

[0066]Specifically, it uses a specific zero-sequence voltage threshold condition U₀=0.15U_n as a reference, and finally determines the exact time t_f when the fault occurs by reversely searching the sampling points that meet the specific conditions.

[0067]Its functions and effects include simplify calculation: through simple voltage threshold comparison, quickly lock the time range of potential faults and reduce the computational complexity, improve accuracy, reverse search combined with multiple constraints can effectively avoid time misjudgment caused by random noise or false measurement, enhance adaptability, this method is not only suitable for high impedance faults, but also suitable for judging other types of faults, and has strong universality.

[0068]The decomposing discrete Fourier includes: performing Fourier expansion on discrete time series I_0j[n] with length N:

$I_{0j}\left[n\right]=\mathrm{DFT}\left(I_{0j}\left[0\right]\right)+\sum _{k=1}^{N/2-1}\mathrm{DFT}\left(I_{0j}\left[n\right]\right)e^{\frac{j2\pi kn}{N}}+\mathrm{DFT}\left(I_{0j}\left[N/2\right]\right)e^{j\pi n}+\sum _{k=N/2+1}^{N-1}\mathrm{DFT}\left(I_{0j}\left[n\right]\right)e^{\frac{j2\pi kn}{N}}$

wherein I_0j[n] is the discrete time series of the zero-sequence current signal at the j-th of the monitoring points, DFT (I_0j[0]) is a result of a discrete Fourier transform and indicating a frequency component of the signal in a frequency domain, N is an actual sampling point of the intercepted signal I_0j, K is a frequency index, indicating a number of the current frequency component, and the value range is 1≤k<N/2−1, n is a index of a discrete point in time series, and the value range is 1≤n≤N;

$e^{\frac{j2\pi kn}{N}}$

is a phase rotation factor in Fourier transform and indicating the phase information of frequency components; and taking a real part of the above results as a signal component for subsequent feature extraction.

[0069]Specifically, the realization method of discrete Fourier transform is defined, which is used to extract high-frequency and low-frequency features by expanding and decomposing the frequency components of the signal.

[0070]Its function and effect are embodied in multi-band analysis, the zero-sequence current signal is decomposed into frequency components by Fourier transform, which lays the foundation for subsequent feature extraction and analysis; preserving signal characteristics: adopting the method of real part feature extraction can reduce the computational complexity while maintaining the original characteristics of the signal; wide applicability: this step is not only applicable to high impedance fault in distribution network, but also applicable to other fault detection scenarios involving frequency domain analysis.

${\mathrm{IMF}}_1=I_{0,j}^{\mathrm{Re}}.$

[0076]Specifically, the extraction process of the first natural mode function (IMF₁) is described, the core of which is to obtain the main characteristic components of the signal through frequency scanning.

$\begin{array}{c}áI_{0j}^{\mathrm{Re},0.5T},\stackrel{V}{\mathrm{añ}}=\mathrm{arc}\cos \frac{I_{0j}^{\mathrm{Re},0.5T}\stackrel{V}{ga}}{I_{0j}^{\mathrm{Re},0.5T},\stackrel{V}{a}}\\ áI_{0j}^{\mathrm{Re},0.5T},\stackrel{V}{\mathrm{bñ}}=\mathrm{arc}\cos \frac{I_{0j}^{\mathrm{Re},0.5T}\stackrel{V}{gb}}{I_{0j}^{\mathrm{Re},0.5T},\stackrel{V}{b}}\end{array}.$

[0085]Specifically, the construction method of feature vectors is proposed. By designing orthogonal vector groups A and B, the real signal of the first natural mode function (IMF₁) is projected to generate a feature vector with physical significance.

[0090]In the S4 step, a result of a feature vector generation is expressed in polar coordinates: S403, obtaining a set of three-dimensional vectors as follows:

$M_j=\text{?}❘I_{0j}^{\mathrm{Re},0.5T}❘áI_{0j}^{\mathrm{Re},0.5T},\stackrel{V}{\mathrm{añ}}áI_{0j}^{\mathrm{Re},0.5T},\stackrel{V}{\mathrm{bñ}}+p\text{?};$$\text{?}\text{indicates text missing or illegible when filed}$

[0091]which can be correspondingly expressed as J pairs of vector groups in polar coordinates:

[0096]Specifically, the calculation methods of direction vector and three-dimensional feature vector are further refined, and the polar coordinate representation of feature vector is proposed.

[0097]Its functions and effects include: high-precision modeling: accurately capture the directional characteristics of fault signals through the calculation of direction vectors; geometric representation optimization: the polar coordinate representation of three-dimensional feature vectors enhances the spatial distribution information of signal features and lays the foundation for clustering analysis; improve robustness: polar coordinate representation avoids the deviation caused by coordinate system selection and improves the expression ability of fault signal characteristics.

$S\left(i\right)=\frac{b\left(i\right)-a\left(i\right)}{\max \left\{a\left(i\right),b\left(i\right)\right\}};$

wherein a(i) represents a cohesion of sample points, which is calculated as follows:

$a\left(i\right)=\frac{1}{n-1}\sum _{j\ne i}^n\mathrm{distance}\left(i,j\right)$

wherein j represents other sample point in the same class as sample i, and distance represents distance between i and j, and the smaller a(i) means that the class is more compact, the calculation method of b(i) is similar to that of a(i), but it is necessary to traverse other clusters to get multiple values {b₁(i), b₂(i), b₃(i), . . . , b_m(i)}, and choose the smallest value as the final result;

$S\left(i\right):S\left(i\right)=\{\begin{array}{cc}1-\frac{a\left(i\right)}{b\left(i\right)}& a\left(i\right)<b\left(i\right)\\ 0& a\left(i\right)=b\left(i\right)\\ \frac{b\left(i\right)}{a\left(i\right)}-1& a\left(i\right)>b\left(i\right)\end{array}$

[0104]Specifically, a feature fusion and analysis method based on fuzzy clustering algorithm (FCM) and contour coefficient calculation is defined in detail.

[0105]Its functions and effects include: fault section judgment: by clustering the direction vector, the upstream and downstream distribution of fault points can be quickly identified; optimization of classification effect: the introduction of contour coefficient can quantify the clustering effect and ensure the accuracy of classification results; combining the network topology: combining the clustering results with the network topology structure to further accurately locate the fault section; computational flexibility: fuzzy clustering algorithm has strong adaptability and can handle the distribution of feature vectors in different scenes.

[0106]FIG. 4 shows the polar coordinate form feature vector groups (a) a=90°. (b) a=60°. (c) a=0°. (d) a=−30° under different fault angles, and the vector group with light green shading is the final selected feature vector group for judging high impedance fault, and the other group must be discarded.

Embodiment 2

[0107]Referring to FIG. 1, in the second embodiment of the present application, the present application provides a system for quickly locating the high impedance fault in distribution network based on directional frequency scanning, wherein the system is used for a high-speed location method for the high impedance fault in the distribution network based on the directional frequency scanning, including: signal acquisition module, and the signal acquisition module is used for acquiring a zero-sequence current signal I_0j collected by each of monitoring points of the distribution network and a bus zero-sequence voltage signal U₀ of the distribution network to provide basic data for fault location; fault time determination module, and the fault time determination module is used for analyzing the bus zero-sequence voltage signal U₀ and determining an accurate fault occurrence time t_f by setting a threshold value and a reverse search algorithm; feature extraction module, and the feature extraction module is used for performing discrete Fourier transform on the zero-sequence current signal I_0j and extracting a first natural mode function for reconstructing a fault signal feature; feature vector construction module, and the feature vector construction module is used for generating a reference vector in a high-dimensional space, projecting a first natural mode function to an orthogonal vector group, constructing a three-dimensional feature vector, and intuitively representing a fault feature; and feature fusion and analysis module, and the feature fusion and analysis module is used for analyzing a direction vector based on a fuzzy clustering algorithm, calculating a contour coefficient, and locating a fault section in combination with a network topology.

Embodiment 3

[0108]In the third embodiment of the present application, the present application provides a computer device includes a memory; a processor; and a computer program stored in the memory and operable on the processor, wherein the processor executes the computer program to realize a high-speed location method for high impedance fault in distribution network based on directional frequency scanning.

Embodiment 4

[0109]In the fourth embodiment of the present application, the present application provides a readable storage medium, wherein a computer program is stored on the readable storage medium, and the computer program is executed by a processor to realize a high-speed location method for high impedance fault in distribution network based on directional frequency scanning.

[0110]It should be understood that various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiment, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by an appropriate instruction execution system. For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies or their combination: a discrete logic circuit with a logic gate for implementing a logic function on a data signal, an application specific integrated circuit with a suitable combination logic gate, a programmable gate array (PGA), a field programmable gate array (FPGA), and the like.

[0111]The above is only the preferred embodiment of the present application. If the scope of implementation of the present application cannot be limited, that is, all the equal changes and modifications made according to the scope of this application should still fall within the scope of the present application.